MacDonald's Menu Card Analysis

i'm lovin' it

INDEX

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Intro: MacD, The Golden Arches, The American Dream
Reposrt Summary
Explanatory Data Analysis
1. Overview of Data Set
2. Statistical Overview of Data
3. Unique value in the Category
4. Number of Items in Each Category
5. Plotting Food Category having higest and lowest variety of Item
6. Analysis of Variables with Outliers
7. Correlations between Variables
8. Category of Menu Contributing to max% of Cholesterol
9. Item of the Menu Contributing to the max intake of Sodium

10. Food Item which contributes most amount of Saturated Fat
11. Analysis of Nutrients across Category of Macdonalds Menu

12. Analysis of Carbohydrates vs Sugar
13. Ordering Crispy(fried) chiken or Grilled Chicken
Conclusion

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MacD, The Golden Arches, The American Dream

MacDonald brthers , Richard and Maurice McDonald american food entrepuneurs started the Orginal Macdonald in 1940 at San Barnadino, Califonia with the Usp of "Speedee Service System".By September 13,1961 McDonald's, under the guidance of Ray Kroc, filed for a trademark on a new logo—an overlapping, double-arched "M" symbol.That Marks the start of the Golden Arches and became a prominent milestone in the capter of "American Dreams".

As of 2021 MacDoland marks its presence in more than 100 countries, serving millions of people everyday. But how did Macdoland become what it is today. The business model is simple, FRANCHISES.

Firstly, Ray Croc had simple moto of "Maintaining Quality","Maintaining Service Consistancy","Maintaining Cleanliness", "Maintaining Value" across all the franchise outlet. He went on to establish the Hamburger University to teach the franchise to main the consistence.

Secondly Macdonald have a incorgible behavior of adapting to the culture of the country. Example India were it amazingly adapted to the vegeterian maintaing the same Quality,Service,cleanliness and Value of the food.

Thirdly, Brand presence "I'm lovin it" is an emotion with people of many country which reminds them of their childhood.

Fourthly, its ability to innovate, 1975 Macdolads invented the drive away for the customers were you do not need to get out of the car to order to take your food.in the mordern world of technology they also have adpated quickly to idea of delivery with better packaging than most of their compititors.

But Most of Macdoland Profit do not come from selling burgers but from rent!!! How ????

Macdonald according to most analyst is reccesion free. Its actually the 5th largest realestate company in the world.85% of the Macdonalds are owned by people who are willing to take a franchise of the brand name in return of a fee(rent) and Macdonald contract comes with multile clause like vendors from whom you can buy the raw materials even if they are higher price. Thats how one doesnt gets to undertsand the difference of taste of food from outlet to outlet.

Macdonald have a decade of exeprince in buying and selling of plot. So, the person who is franchising the brand name has to open the outlet in a prime location owned and choosed by Macdonalds, in return of which the franchise give Macdonald rent and franchise fee and equipment fee.And if the franchise at a location fails to run the business or draw traffic Macdonald Corporation simply find a new tenant("Franchise") to run the business or sell the land.

Report Sumary

The Data set provide is the nutrition value of the all the category of food and beverages sold in MacDonald and all the items under each categories of Food and Beverages.The Data was analysed using Python and Pythons Library.Inference to the Analysis done is also shared along with the graphical representation.

The below analysis will involve the use of Pandas, Numpy, Matplotlip, Seaborn, Scipy, Pyplot and Plotly express.

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Explanatory Data Analysis

#IMPORTING LIBRARIES OF PYTHON

import matplotlib as mt
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import pandas as pd
import numpy as np
import scipy.stats as stat
import plotly.express as px
import plotly.graph_objects as go
#import plotly.offline as pyo
#pyo.init_notebook_mode()
import warnings
warnings.filterwarnings('ignore')
sns.set_style('darkgrid')

# IMPORTING THE DATA SET

menu_df=pd.read_csv('Mcdonald .csv')

1. Checking the overview (information) the data set

#UNDERSTANDING THE NUMBER OF COLUMNS AND THE ROWS IN THE DATASET

print("Number of Rows in the data set : {}".format(menu_df.shape[0]))
print("Number of Columns in the data set : {}".format(menu_df.shape[1]))

Number of Rows in the data set : 260
Number of Columns in the data set : 25

#THE BELOW LINE OF CODE WILL HELP TO SEE ALL THE COLUMNS IN THE DATA SET BY SETTING THE MAX DISPLAY TO NUMBER OF COLS

pd.set_option('display.max_columns',25)
pd.set_option('display.max_rows',25)

#PRINTING THE HEAD OF THE DATASET TO GET A PRELIMINARY UNDERSTANDING OF THE COLUMNS AND THE VALUE

menu_df.head()

	Category	Item	Serving Size(in oz)	Serving Size(g)	Calories	Calories from Fat	Total Fat	Total Fat (% Daily Value)	Saturated Fat	Saturated Fat (% Daily Value)	Trans Fat	Cholesterol	Cholesterol (% Daily Value)	Sodium	Sodium (% Daily Value)	Carbohydrates	Carbohydrates (% Daily Value)	Dietary Fiber	Dietary Fiber (% Daily Value)	Sugars	Protein	Vitamin A (% Daily Value)	Vitamin C (% Daily Value)	Calcium (% Daily Value)	Iron (% Daily Value)
0	Breakfast	Egg McMuffin	4.8	136	300	120	13.0	20	5.0	25	0.0	260	87	750	31	31	10	4	17	3	17	10	0	25	15
1	Breakfast	Egg White Delight	4.8	135	250	70	8.0	12	3.0	15	0.0	25	8	770	32	30	10	4	17	3	18	6	0	25	8
2	Breakfast	Sausage McMuffin	3.9	111	370	200	23.0	35	8.0	42	0.0	45	15	780	33	29	10	4	17	2	14	8	0	25	10
3	Breakfast	Sausage McMuffin with Egg	5.7	161	450	250	28.0	43	10.0	52	0.0	285	95	860	36	30	10	4	17	2	21	15	0	30	15
4	Breakfast	Sausage McMuffin with Egg Whites	5.7	161	400	210	23.0	35	8.0	42	0.0	50	16	880	37	30	10	4	17	2	21	6	0	25	10

menu_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 260 entries, 0 to 259
Data columns (total 25 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   Category                       260 non-null    object 
 1   Item                           260 non-null    object 
 2   Serving Size(in oz)            260 non-null    float64
 3   Serving Size(g)                260 non-null    object 
 4   Calories                       260 non-null    int64  
 5   Calories from Fat              260 non-null    int64  
 6   Total Fat                      260 non-null    float64
 7   Total Fat (% Daily Value)      260 non-null    int64  
 8   Saturated Fat                  260 non-null    float64
 9   Saturated Fat (% Daily Value)  260 non-null    int64  
 10  Trans Fat                      260 non-null    float64
 11  Cholesterol                    260 non-null    int64  
 12  Cholesterol (% Daily Value)    260 non-null    int64  
 13  Sodium                         260 non-null    int64  
 14  Sodium (% Daily Value)         260 non-null    int64  
 15  Carbohydrates                  260 non-null    int64  
 16  Carbohydrates (% Daily Value)  260 non-null    int64  
 17  Dietary Fiber                  260 non-null    int64  
 18  Dietary Fiber (% Daily Value)  260 non-null    int64  
 19  Sugars                         260 non-null    int64  
 20  Protein                        260 non-null    int64  
 21  Vitamin A (% Daily Value)      260 non-null    int64  
 22  Vitamin C (% Daily Value)      260 non-null    int64  
 23  Calcium (% Daily Value)        260 non-null    int64  
 24  Iron (% Daily Value)           260 non-null    int64  
dtypes: float64(4), int64(18), object(3)
memory usage: 50.9+ KB

INFERENCE

1. There are no Null values in the data set.

2. Only Category and Items are categorical variables and other 22 variables are numerical variable.

3. columns like Total Fat, Cholesterol, etc. have 2 columns where the first one denotes the quantity in grams and “% Daily Value” can tell you if serving of food is high or low in a nutrient.

---

2. Understanding of the statistically overview of the data

print('Statistical Overview of the Dataset'.center(75, ' '))
menu_df.describe().T

                    Statistical Overview of the Dataset                    

	count	mean	std	min	25%	50%	75%	max
Serving Size(in oz)	260.0	12.803077	7.054481	1.0	6.775	12.0	16.00	32.0
Calories	260.0	368.269231	240.269886	0.0	210.000	340.0	500.00	1880.0
Calories from Fat	260.0	127.096154	127.875914	0.0	20.000	100.0	200.00	1060.0
Total Fat	260.0	14.165385	14.205998	0.0	2.375	11.0	22.25	118.0
Total Fat (% Daily Value)	260.0	21.815385	21.885199	0.0	3.750	17.0	35.00	182.0
Saturated Fat	260.0	6.007692	5.321873	0.0	1.000	5.0	10.00	20.0
Saturated Fat (% Daily Value)	260.0	29.965385	26.639209	0.0	4.750	24.0	48.00	102.0
Trans Fat	260.0	0.203846	0.429133	0.0	0.000	0.0	0.00	2.5
Cholesterol	260.0	54.942308	87.269257	0.0	5.000	35.0	65.00	575.0
Cholesterol (% Daily Value)	260.0	18.392308	29.091653	0.0	2.000	11.0	21.25	192.0
Sodium	260.0	495.750000	577.026323	0.0	107.500	190.0	865.00	3600.0
Sodium (% Daily Value)	260.0	20.676923	24.034954	0.0	4.750	8.0	36.25	150.0
Carbohydrates	260.0	47.346154	28.252232	0.0	30.000	44.0	60.00	141.0
Carbohydrates (% Daily Value)	260.0	15.780769	9.419544	0.0	10.000	15.0	20.00	47.0
Dietary Fiber	260.0	1.630769	1.567717	0.0	0.000	1.0	3.00	7.0
Dietary Fiber (% Daily Value)	260.0	6.530769	6.307057	0.0	0.000	5.0	10.00	28.0
Sugars	260.0	29.423077	28.679797	0.0	5.750	17.5	48.00	128.0
Protein	260.0	13.338462	11.426146	0.0	4.000	12.0	19.00	87.0
Vitamin A (% Daily Value)	260.0	13.426923	24.366381	0.0	2.000	8.0	15.00	170.0
Vitamin C (% Daily Value)	260.0	8.534615	26.345542	0.0	0.000	0.0	4.00	240.0
Calcium (% Daily Value)	260.0	20.973077	17.019953	0.0	6.000	20.0	30.00	70.0
Iron (% Daily Value)	260.0	7.734615	8.723263	0.0	0.000	4.0	15.00	40.0

INFERENCE

1. The above table helps to identify the continuos number columns as the describe function by default only displays the continous number.

2. The table also provides crucial data about the Mean, Standart devition, min,max, 1st quartlie, 2nd quartile and 3rd quartile of the variables

---

3. Unique values in the Category Column

#UNIQUE CATEGORY ITEMS

print("The Unique Category Available in Macdonad are: \n\n {} ".format(menu_df.Category.unique()))

The Unique Category Available in Macdonad are: 

 ['Breakfast' 'Beef & Pork' 'Chicken & Fish' 'Salads' 'Snacks & Sides'
 'Desserts' 'Beverages' 'Coffee & Tea' 'Smoothies & Shakes'] 

4. Number of Items in each Category (Question 1)

Item_count=pd.DataFrame(menu_df['Category'].value_counts()).rename(columns={"Category": "No. of Item"})
Item_count

	No. of Item
Coffee & Tea	95
Breakfast	42
Smoothies & Shakes	28
Chicken & Fish	27
Beverages	27
Beef & Pork	15
Snacks & Sides	13
Desserts	7
Salads	6

5. Ploting Food Category having the higest and lowest of the variety of Items (Question 1)

Item_count.plot(kind="bar",facecolor='green',figsize=(9,7),legend=False);

xticks=plt.xticks(rotation=90,family='serif',fontsize=12)
yticks=plt.yticks(family='serif',fontsize=12)

plt.xlabel('Categories of food served',font='serif',fontsize=15)
plt.ylabel('Count of Items',font='serif',fontsize=15)
plt.title("Count of Item in each Category\n\n",fontsize=20);

INFERENCE( from 4 & 5 )

1. Coffee & Tea have the highest variety of Items.

2. Salad has the least variety of items.So may be people of diet and looking for salads, Macdonalds in not a good place to visit. And vice versa those people are not the targeted audience of Macdonald .

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6. Analysis of the columns to find which variables have outliers (Question 2)

To find the outliers in the variable we will only be using the columns displayed in the Statistics Overview Table as by default describe function returns statistical over view of the columns which contains continous numbers. And Outliers can only be predicted for continous number variable.

Secondly, Analysing variables like Total Fat (% Daily Value), Cholesterol (% Daily Value) etc are derived variables from Total Fat, Cholesterol etc respectly. So there is no pooint in analysing the same as if Total Fat contains outliers, Total Fat (% Daily Value) will also contain outliers

fig,axes=plt.subplots(6,3,figsize=(15,30))
plt.tight_layout(pad=10)

#BOXPLOT FOR SERVING SIZE IN OZ

axes[0,0].set_title('Seving Size',fontsize=20);
sns.boxplot(data=menu_df["Serving Size(in oz)"],ax=axes[0,0]);

#BOXPLOT FOR CALORIES

axes[0,1].set_title('Calories',fontsize=20);
sns.boxplot(data=menu_df["Calories"],ax=axes[0,1]);

#BOXPLOT FOR CALORIES FROM FAT

axes[0,2].set_title('Calories from Fat',fontsize=20);
sns.boxplot(data=menu_df["Calories from Fat"],ax=axes[0,2]);

#BOXPLOT FOR TOTAL FAT

axes[1,0].set_title('Total Fat',fontsize=20);
sns.boxplot(data=menu_df["Total Fat"],ax=axes[1,0]);

#BOXPLOT FOR SATURATED FAT

axes[1,1].set_title('Saturated Fat',fontsize=20);
sns.boxplot(data=menu_df["Saturated Fat"],ax=axes[1,1]);

#BOXPLOT FOR TRANS FAT

axes[1,2].set_title('Trans Fat',fontsize=20);
sns.boxplot(data=menu_df["Trans Fat"],ax=axes[1,2]);

#BOXPLOT FOR CHOLESTEROL

axes[2,0].set_title('Cholesterol',fontsize=20);
sns.boxplot(data=menu_df["Cholesterol"],ax=axes[2,0]);

#BOXPLOT FOR SODIUM

axes[2,1].set_title('Sodium',fontsize=20);
sns.boxplot(data=menu_df["Sodium"],ax=axes[2,1]);

#BOXPLOT FOR CARBOHYDRATE

axes[2,2].set_title('Carbohydrates',fontsize=20);
sns.boxplot(data=menu_df["Carbohydrates"],ax=axes[2,2]);

#BOXPLOT FOR DIETARY FIBER

axes[3,0].set_title('Dietary Fiber',fontsize=20);
sns.boxplot(data=menu_df["Dietary Fiber"],ax=axes[3,0]);

#BOXPLOT FOR SUGAR

axes[3,1].set_title('Sugars',fontsize=20);
sns.boxplot(data=menu_df["Sugars"],ax=axes[3,1]);

#BOXPLOT FOR PROTEIN

axes[3,2].set_title('Protein',fontsize=20);
sns.boxplot(data=menu_df["Protein"],ax=axes[3,2]);

#BOXPLOT FOR VITAMIN A

axes[4,0].set_title('Vitamin A (% Daily Value)',fontsize=20);
sns.boxplot(data=menu_df["Vitamin A (% Daily Value)"],ax=axes[4,0]);

#BOXPLOT FOR VITAMIN C

axes[4,1].set_title('Vitamin C (% Daily Value)',fontsize=20);
sns.boxplot(data=menu_df["Vitamin C (% Daily Value)"],ax=axes[4,1]);

#BOXPLOT FOR CALCIUM

axes[4,2].set_title('Calcium (% Daily Value)',fontsize=20);
sns.boxplot(data=menu_df["Calcium (% Daily Value)"],ax=axes[4,2]);

#BOXPLOT FOR IRON

axes[5,0].set_title('Iron (% Daily Value)',fontsize=20);
sns.boxplot(data=menu_df["Iron (% Daily Value)"],ax=axes[5,0]);

INFERENCE

As per the Box Plot analysis done above the following variable has Outliers:

1. Serving Size

2. Calories

3. Calories from Fat

4. Total Fat

5. Trans Fat

6. Cholesterol

7. Sodium

8. Carbohydrates

9. Sugars

10. Protein

11. Vitamin A

12. Vitamin C

13. Calcium

14. Iron

---

7. Corrlation between Variables (Question 3)

corr_mat=menu_df.corr()
plt.figure(figsize=(65,40));
plt.xticks(rotation=90,size=50)
plt.yticks(size=50)

# Generates a mask for upper traingle
mask = np.triu(np.ones_like(corr_mat, dtype=bool))
sns.heatmap(corr_mat, 
            annot=True, 
            mask = mask, 
            annot_kws={'size': 40});

The above table shows the correlation between all the variables in the data set but we are generally concernd with Calories, Protein, Fat, Carbohydrate, Sugar and Dietary Fiber. So lets see a consentrated Heatmap to find the correlation betwee the above variable so, that it helps to choose our next Macdonald menu when we are on diet.

cols = ['Calories','Cholesterol','Trans Fat','Sugars','Dietary Fiber']
cm = np.corrcoef(menu_df[cols].values.T)
sns.set(font_scale = 1.5)
hm = sns.heatmap(cm,cbar = True, 
                 annot = True, 
                 annot_kws = {'size':15}, 
                 yticklabels = cols, 
                 xticklabels = cols)

8. Category of Menu which contributes to maximum % of Cholesterol (Question 4)

table_chol=pd.pivot_table(menu_df,values="Cholesterol (% Daily Value)",index=['Category'])
table_chol

	Cholesterol (% Daily Value)
Category
Beef & Pork	28.933333
Beverages	0.185185
Breakfast	50.952381
Chicken & Fish	25.222222
Coffee & Tea	9.378947
Desserts	4.857143
Salads	17.333333
Smoothies & Shakes	14.714286
Snacks & Sides	6.230769

INFERENCE

1. Breakfast Category contribute to most cholestrol. So, when you are entering the Macdolads for a breakfast, think twice !!!

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9. Item of Menu which contributes to maximum to Sodium Intake (Question 5)

table_sod=pd.pivot_table(menu_df,values="Sodium",index=['Item']).sort_values(by='Sodium',ascending=False)
table_sod.head()

	Sodium
Item
Chicken McNuggets (40 piece)	3600
Big Breakfast with Hotcakes and Egg Whites (Large Biscuit)	2290
Big Breakfast with Hotcakes (Large Biscuit)	2260
Big Breakfast with Hotcakes and Egg Whites (Regular Biscuit)	2170
Big Breakfast with Hotcakes (Regular Biscuit)	2150

INFERENCE

Chicken McNuggets has the most amount of Sodium.

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10. 4 Foods(Items) which contains most amount of Sarurated Fa (Question 6)

table_satfat=pd.pivot_table(menu_df,values="Saturated Fat",index=['Item']).sort_values(by='Saturated Fat',ascending=False)
table_satfat.head(10)

	Saturated Fat
Item
McFlurry with M&M’s Candies (Medium)	20.0
Big Breakfast with Hotcakes (Large Biscuit)	20.0
Chicken McNuggets (40 piece)	20.0
Frappé Chocolate Chip (Large)	20.0
Double Quarter Pounder with Cheese	19.0
Big Breakfast with Hotcakes (Regular Biscuit)	19.0
Big Breakfast (Large Biscuit)	18.0
Frappé Mocha (Large)	17.0
Frappé Chocolate Chip (Medium)	17.0
Big Breakfast (Regular Biscuit)	17.0

INFERENCE

The above table 4 Food Item which contributes to maximum number of Saturated Fat are :

1. McFlurry with M&M’s Candies (Medium)

2. Big Breakfast with Hotcakes (Large Biscuit)

3. Chicken McNuggets (40 piece)

4. Frappé Chocolate Chip (Large)

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11. Analysis of Major Nutrients across Category of Macdonald Food Menu

Under the category of major nutrients comes Carbohydrates, Proteins, Fats, Vitamins, Minerals, Dietary fibre, Water.But as all the neccessary variables are not available for us we will be doing the analysis based on Proteins, Total Fat,Vitamin A, Dietary Fiber only.

10.1. Analysis of Proteins

protein=pd.DataFrame(menu_df.groupby('Category')['Protein'].mean())
        
colors=['gray']*9
colors[3]='Green'
fig = go.Figure(data=[go.Bar(
    x=protein.index,
    y=protein['Protein'],
    marker_color=colors
)])
fig.update_yaxes(title='Average Protein')
fig.update_layout(width=900,height=700,
                 title={
                        'text': "Analysis of Protein",
                        'y':.9,
                        'x':.5,
                        'xanchor': 'center',
                        'yanchor': 'top'})

INFERENCE

Chicken and Fish Category has most amount of Protien.

---

10.2. Analysis of Total Fat

fat=pd.DataFrame(menu_df.groupby('Category')['Total Fat'].mean())
        
colors=['gray']*9
colors[2]='Orange'
fig = go.Figure(data=[go.Bar(
    x=fat.index,
    y=fat['Total Fat'],
    marker_color=colors
)])
fig.update_yaxes(title='Avgerage Total Fat')
fig.update_layout(width=900,height=700,
                 title={
                        'text': "Analysis of Total Fat",
                        'y':.9,
                        'x':.5,
                        'xanchor': 'center',
                        'yanchor': 'top'})

INFERENCE

Break Fast Category has most amount of Fat.

---

10.2. Analysis of Vitamin A (% Daily Value)

Vit=pd.DataFrame(menu_df.groupby('Category')['Vitamin A (% Daily Value)'].mean())
        
colors=['gray']*9
colors[6]='Skyblue'
fig = go.Figure(data=[go.Bar(
    x=Vit.index,
    y=Vit['Vitamin A (% Daily Value)'],
    marker_color=colors
)])
fig.update_yaxes(title='Avgerage Total Fat')
fig.update_layout(width=900,height=700,
                 title={
                        'text': "Analysis of Vitamin A",
                        'y':.9,
                        'x':.5,
                        'xanchor': 'center',
                        'yanchor': 'top'})

INFERENCE

Salad Category has most amount of Vitamin A.

---

10.2. Analysis of Dietary Fiber

DtFiber=pd.DataFrame(menu_df.groupby('Category')['Dietary Fiber'].mean())
        
colors=['gray']*9
colors[6]='Brown'
fig = go.Figure(data=[go.Bar(
    x=DtFiber.index,
    y=DtFiber['Dietary Fiber'],
    marker_color=colors
)])
fig.update_yaxes(title='Avgerage Dietary Fiber')
fig.update_layout(width=900,height=700,
                 title={
                        'text': "Analysis of Dietary Fiber",
                        'y':.9,
                        'x':.5,
                        'xanchor': 'center',
                        'yanchor': 'top'})

INFERENCE

Salad Category has most amount of Dietary Fiber.

---

NOTE : The Above anlaysis done on mean value of Protein, Vintamin A, dietary fibaer, Total Fat may not be showing the right picture. Because Mean is a statistical measurement which is susceptible to Outliers. The degree of true of the above graphs can be cross verified with the help of A swarmp plot. from previous outliers analysis we know that other than Dietary fiber all the others such as Protein, Total fat and vitamin A has outliers. So, Not totaly discarding the above bar graph analysis or pivot, we will look into the degree of truth in the above bar graphs using a swarm plot.

fig,axes=plt.subplots(2,2,figsize=(20,20))
plt.tight_layout(pad=8)

axes[0,0].set_title('Category and Protein');
ax1= sns.swarmplot(x="Category", y="Protein", data=menu_df,ax=axes[0,0]);
ax1.set_xticklabels(ax1.get_xticklabels(),rotation=90);

axes[0,1].set_title('Category and Dietary Fiber');
ax2= sns.swarmplot(x="Category", y="Dietary Fiber", data=menu_df,ax=axes[0,1]);
ax2.set_xticklabels(ax1.get_xticklabels(),rotation=90);

axes[1,0].set_title('Category and Vitamnin A');
ax3= sns.swarmplot(x="Category", y="Vitamin A (% Daily Value)", data=menu_df,ax=axes[1,0]);
ax3.set_xticklabels(ax1.get_xticklabels(),rotation=90);


axes[1,1].set_title('Category and Total Fat');
ax4= sns.swarmplot(x="Category", y="Total Fat", data=menu_df,ax=axes[1,1]);
ax4.set_xticklabels(ax1.get_xticklabels(),rotation=90);

INFERENCE

1. From the first plot it is clear that chiken and fish category for the protein has an outleir which is far beyond the concentration of protein around the mean. That means the bar graph(based of mean value is actually not totaaly true). If we consider the spread of data point in beef and pork category with chiken and fish category keeping in mind the chiken and fish category has outliers, then beef and pork category has most number of protein concentration compared to chiken.

2.Total Fat according to the "Category and Total Fat " graph do not show any outliers, so we can take the bar grap data and state that at an average breakfast have the highest amount of fat. Going further deep we are trying to find the second highest contain of fat, that from the bar graph is chiken and fish which is a bit decisive as is the swarmplot clearly shows that the chicken and fish category has outliers.

3. Salad has highest amount of Vitamin A and Dietary fibers is a correct conclusion from teh swarm plot and the barplot as the dat points cannot be taken an absolute outliers.

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12. Analysis of Carbohydrate and Sugar

Difference between Carbohydrates and Sugar-When people eat a food containing carbohydrates, the digestive system breaks down the digestible ones into sugar, which enters the blood. As blood sugar levels rise, the pancreas produces insulin, a hormone that prompts cells to absorb blood sugar for energy or storage.

So according to the above difference Sugar is derivative of Carbohydrate. In that case, if we plot ascattered plot carbohydrates vs sugar will show a relation where when carbohydrates are increasing the sugar will also incerease.

fig=px.scatter(menu_df, x="Carbohydrates", y="Sugars", trendline="ols",color='Carbohydrates')
fig.update_traces(marker_size=5)
fig.update_layout(width=900,height=700,
                 title={
                        'text': "Carbohydrate vs Sugar",
                        'y':.9,
                        'x':.5,
                        'xanchor': 'center',
                        'yanchor': 'top'});
fig.show()

INFERENCE

Defintely the statement before the analysis is true, Sugar is a derivative of Carbohydrate from the above chart.Carbohydrate increase so the sugar contain also increases comparatively

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13. Ordering Crispy(Fried) or Grilled chicken

The below analysis figures out whether to order crispy chiken or grilled chiken from a Nutrition point of view on basis of Calories and Protein.

1. On Basis of Calories

#1. Average Calories Calculation for Crispy Chicken

fry = menu_df[menu_df['Item'].str.contains('Crispy Chicken')]
fry_cal = pd.DataFrame({'Item': fry.Item, 'Calories': fry.Calories})
avg_fry_cal = round(fry.Calories.mean(),2)
print("Average calories on Crispy Chicken category is {}".format(avg_fry_cal))



#2. Average Calories Calculation for Grilled Chicken
grilled = menu_df[menu_df['Item'].str.contains('Grilled Chicken')]
grilled_cal = pd.DataFrame({'Item': grilled.Item, 'Calories': grilled.Calories})
avg_grilled_cal = round(grilled.Calories.mean(), 2)
print("Average calories on Grilled Chicken category is {} \n\n".format( avg_grilled_cal))



#plotting a graphy based on the observation
avg_cal = pd.DataFrame({'Categories':['Crispy Chicken', 'Grilled Chicken'], 
                        'Avg Calories': [avg_fry_cal, avg_grilled_cal]})
avg_cal.plot.bar(x = 'Categories', y = 'Avg Calories', figsize=(7,5));

Average calories on Crispy Chicken category is 520.0
Average calories on Grilled Chicken category is 386.92 

INFERENCE

So, If someone are looking for high Calories he or she can go for items with Crispy Chicken rather than Grilled Chiken.

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2. On Basis of Protien

fry2 = menu_df[menu_df['Item'].str.contains('Crispy Chicken')]
fry2_cal = pd.DataFrame({'Item': fry2.Item, 'Protein': fry2.Calories})
avg_fry2_cal = round(fry2.Protein.mean(),2)
print("Average protein on Crispy Chicken category is {}".format(avg_fry2_cal))



#2. Average Calories Calculation for Grilled Chicken
grilled2 = menu_df[menu_df['Item'].str.contains('Grilled Chicken')]
grilled2_cal = pd.DataFrame({'Item': grilled2.Item, 'Protein': grilled2.Protein})
avg_grilled2_pro = round(grilled2.Protein.mean(), 2)
print("Average protein on Grilled Chicken category is {} \n\n".format( avg_grilled2_pro))



#plotting a graphy based on the observation
avg_pro = pd.DataFrame({'Categories':['Crispy Chicken', 'Grilled Chicken'], 
                        'Avg Protein': [avg_fry2_cal, avg_grilled2_pro]})
avg_pro.plot.bar(x = 'Categories', y = 'Avg Protein', figsize=(7,5));

Average protein on Crispy Chicken category is 24.93
Average protein on Grilled Chicken category is 28.62 

INFERENCE

There is no much difference in protien contain of both the chicken category, but if someone is looking for higher protein count they can go for Grilled Chiken.

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There is a difference between Correlation and Causation

The Golden Arches Theory states, "Countries with Macdolands did not go to war( or conflict) after the Macdonald was established" which doesnt means that we can derive tha establishing Macdonalds in every country will lead to world peace.

Data is a vast world where we need to think and act and not otherway round of acting and then thinking. WIth the same I would like to conclude my report.

Thank You

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